Enabling geovisual analytics of health data using a server-side approach

Abstract

Geovisual analytics can enable a user to explore multivariate spatio-temporal health datasets and to understand spatial distribution of diseases especially in relation to external factors that may influence outbreaks. External data are presently distributed using geo web services. Web services are used in health mainly to present results leading to a supplier-driven service model limiting the exploration of health data. In this paper, we illustrate server-side approach of designing a geovisual analytics environment that allows user-driven geovisual analytics. The server-side combines a data query, processing technique, and styling methodology to rapidly visually summarize properties of a dataset. We illustrate this functionality on a typical workflow used by a health researcher and demonstrate analytical functionality in cases where a consistent classification and styling scheme is needed across dynamically aggregated multivariate spatio-temporal datasets. Since the framework builds on the existing Open Geospatial Consortium web mapping standards, it integrates the existing geo web services as well as stand-alone health data repositories into an infrastructure that allows combination and interactive exploration of these heterogeneous datasets in a visual environment.

Keywords:

• Geovisual analytics
• server side
• health research
• geo web services
• user-driven analysis

ORCID

Ulanbek Turdukulov http://orcid.org/0000-0002-8551-497X

Notes

1. OGC standards. http://www.opengeospatial.org/standards/l, accessed 12 January 2014

2. Eight spatial scales are shown in Figure 4. Note that boundaries of these regions do not necessarily overlap.

3. GeoServices REST Specification (ESRI). http://www.esri.com/industries/landing-pages/geoservices/geoservices, accessed 12 January 2014

4. https://geodacenter.asu.edu/pysal. Accessed 12 January 2015

Additional information

Funding

This work has been supported by the Cooperative Research Centre for Spatial Information, whose activities were funded by the Australian Commonwealth Cooperative Research Centres Programme. This work is part of a larger body of work for which a provisional patent has been applied.